Natural language based service selection system and method, service query system and method

ABSTRACT

The present invention relates to a natural language based service selection system for complementing incomplete queries, which comprises a semantic analyzing device which analyzes an incomplete query from a user semantically, a service selecting device which complements the incomplete query based on the semantic-analyzed query so as to acquire the corresponding selected service, and a retrieving device which retrieves an answer according to the selected service. The present invention also relates to a natural language based service selection to method as well as a service query system and method thereof, and thus can process an incomplete query from a user and provide a selected service.

BACKGROUND OF THE INVENTION

1. Field of Invention

The present invention relates to the field of natural languageprocessing, and in particular to a natural language based serviceselection system and method as well as a service query system andmethod, which can complement incomplete queries so as to obtain aselected service and provide the corresponding query answer.

2. Description of Prior Art

The existing service selection system based on natural language allows auser to query various services in natural language, and then the systemselects any service corresponding to the user's query from theseservices and feeds the answer back to the user. Such conventionalservice selection system based on natural language, however, can processonly a complete natural language query from the user. If the user entersan incomplete query, that is, the query lacks some essential parameters,the system has difficulty in effectively handling such query, especiallyin finding the lost part of the query.

There have been some natural language based service selection systems,which can analyze and retrieve a service database according to the queryinputted by a user so as to select a service corresponding to the userquery from various service.

Patent Application No. JP2002351913 proposes a method in which a webservice having optimal waiting time can be selected from all types ofweb services according to the history of user access to these webservices (which particularly contains user name, longest waiting time,service type, latest access time, etc.) so as to avoid excessive load onnetwork and service.

Patent Application No. JP2004054781 discloses a method which canextracts key words for retrieval from a user query in natural languageand then select from various services the service corresponding to thekey words for retrieval.

Patent Application No. JP2004288118 provides a method which can, basedon service register data supplied by a service provider, select not onlya service corresponding to a user query but also other services relevantto the service from a plurality of services.

SUMMARY OF THE INVENTION

The present invention is made to address the above problems. The objectof the present invention is to process effectively an incomplete query.In other word, even though a query entered by the user is not complete,the invention can process it accordingly to obtain a selected serviceand thus a query answer.

According to the first aspect of the present invention, a naturallanguage based service selection system for complementing incompletequeries is provided, comprising a semantic analyzing device whichanalyzes an incomplete query from a user semantically, a serviceselecting device which complements the incomplete query based on thesemantic-analyzed query so as to acquire the corresponding selectedservice, and a retrieving device which retrieves an answer according tothe selected service.

According to the second aspect of the present invention, a naturallanguage based service selection method for complementing incompletequeries is provided, comprising a semantic analyzing step of analyzingan incomplete query from a user semantically, a service selecting stepof complementing the incomplete query based on the semantic-analyzedquery so as to acquire the corresponding selected service, and aretrieving step of retrieving an answer according to the selectedservice.

According to the third aspect of the present invention, a query systemis provided, comprising a query receiver which receives a user query, asemantic analyzing device which parses the user query and semanticallyanalyzes the query, a service selecting device which complements theincomplete query based on the semantic-analyzed query so as to acquirethe corresponding selected service, a retrieving device which retrievesan answer according to the selected service, and an answer sender whichsends the answer to the user.

According to the fourth aspect of the present invention, a query methodis provided, comprising a query receiving step of receiving a userquery, a semantic analyzing step of parsing the user query andsemantically analyzes the query, an service selecting step ofcomplementing the incomplete query based on the semantic-analyzed queryso as to acquire the corresponding selected service, a retrieving stepof retrieving an answer according to the selected service, and an answersending step of sending the answer to the user.

According to the fifth aspect of the present invention, a query systemis provided, comprising a query receiver which receives a user query, asemantic analyzing device which parses the user query and semanticallyanalyzes the query, a determining device which determines whether theuser query is an complete user query, a first service selecting devicewhich performs a process on the complete query so as to acquire a firstselected service, a second service selecting device which complementsthe incomplete query so as to acquire a second selected service, aretrieving device which retrieves an answer according to the firstselected service or the second selected service, and an answer senderwhich sends the answer to the user.

According to the sixth aspect of the present invention, a query methodis provided, comprising a query receiving step of receiving a userquery, a semantic analyzing step of parsing the user query andsemantically analyzes the query, a determining step of determiningwhether the user query is an complete user query, a first serviceselecting step of performing a process on the complete query so as toacquire a first selected service, a second service selecting step ofcomplementing the incomplete query so as to acquire a second selectedservice, a retrieving step of retrieving an answer according to thefirst selected service or the second selected service, and an answersending step of sending the answer to the user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 a is a schematic diagram showing a natural language based serviceselection system according to the present invention;

FIG. 1 b is a flowchart showing a natural language based serviceselection method according to the present invention;

FIG. 2 a is a block diagram showing an example of a service mapping rulebase according to the present invention;

FIG. 2 b is a flowchart showing a method for generating a servicemapping rule base;

FIG. 3 is a block diagram showing an example of a user query historybase according to the present invention;

FIG. 4 a is a block diagram showing an example of a fact base accordingto the present invention;

FIG. 4 b is a schematic diagram showing a known semantic analyzingdevice;

FIG. 5 a is a schematic diagram showing a service selecting deviceaccording to the present invention;

FIG. 5 b is a schematic diagram showing a service selecting methodaccording to the present invention;

FIG. 6 a is a block diagram showing a semi-automatic service selectingsection according to the present invention;

FIG. 6 b is a flowchart showing a semi-automatic service selectingmethod;

FIG. 6 c shows an example of semi-automatic service selection;

FIG. 7 a is a block diagram showing a automatic service selectingsection according to the first embodiment of the present invention;

FIG. 7 b is a flowchart showing a automatic service selecting methodaccording to the first embodiment of the present invention;

FIG. 7 c shows an example of service selection;

FIG. 8 a is a block diagram showing a service selecting sectionaccording to the second embodiment of the present invention;

FIG. 8 b is a flowchart showing a service selecting method according tothe second embodiment of the present invention;

FIG. 8 c shows another example of service selection;

FIG. 9 a is a block diagram showing a service selecting sectionaccording to the third embodiment of the present invention;

FIG. 9 b is a flowchart showing a service selecting method according tothe third embodiment of the present invention;

FIG. 9 c shows still another example of service selection according tothe present invention;

FIG. 10 a is a block diagram showing a natural language based servicequery system according to an embodiment of the present invention;

FIG. 10 b is a schematic diagram showing how the natural language basedservice query system performs query process;

FIGS. 11 a and 11 b shows flowcharts of the first and second embodimentsof a retrieval method, respectively;

FIGS. 12 a and 12 b shows a service selecting device used in a mobileterminal and an ASP, respectively.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Hereafter, a description will be made to the preferred embodiments ofthe present invention with reference to the figures, throughout whichlike elements are denoted by like reference symbols or numbers. In thefollowing description, the details of any known function orconfiguration will not be repeated, otherwise they may obscure thesubject of the present invention.

In general, the existing query device cannot process a query inputted bya user if the query is incomplete and thus cannot provide the user withhis/her expected query answer. The service selection system according tothe present invention, however, can complement an incomplete query froma user and thus retrieve an answer desired by the user. FIG. 1 a shows anatural language based service selection system according to the presentinvention, which comprises a query receiver 10 for receiving a naturallanguage query inputted by a user via a mobile terminal, such as amobile phone, a semantic analyzing device 20 for analyzing the receivednatural language query to obtain a structured semantic analysis result,a service selecting device 30 for determining and complementing the lostcontent in the incomplete query based on the semantic analysis result soas to acquire a selected service, a retrieving device 40 for retrievingan answer according to the selected service, and an answer sender 50 forsending the retrieved answer to the user terminal. The service selectionsystem further comprises a storage device 16, such as a hard disk, whichstores a service mapping rule base 160, a user query history base 162and a fact base 164.

FIG. 1 b shows a flowchart of a natural language based service selectionmethod. At S101, the query receiver 10 receives a natural language querysent from a user via a mobile terminal, such as a mobile phone, andtransmits the query to the semantic analyzing device 20. The semanticanalyzing device 20 analyzes the received natural language query atS102. FIG. 4 b shows a block diagram of a known semantic analyzingdevice, which serves to understand the user's natural language query soas to obtain a structured semantic analysis result as well as includes aquery word division unit 401 and a semantic marking unit 402. The queryword division unit 401 performs word division on the natural languagequery with a word database, such as a dictionary, and then the semanticmarking unit 402 performs semantic marking on the division result basedon a semantic knowledge base so as to generate the semantic analysisresult, which is usually formed of a requirement and a set of parametersand parameter values. Each parameter corresponds to its parameter value.Referring to FIG. 4 b, for example, if the nature language query enteredby the user is “How high is the temperature in Beijing today?”, thisnatural language query is subjected to word division by the query worddivision unit 401 to generate a word division result “How high is thetemperature; in Beijing; today”. Then, the result undergoes semanticanalysis by the semantic marking unit 402. Specifically, according tothe semantic knowledge base, it can be learnt that “Beijing represents aplace” and “today represents a date”. As such, the semantic marking unit402 marks “Beijing” in the natural language query as the parameter valueof the first parameter “place”, and “today” as the parameter value ofthe second parameter “date”. It further extracts the interrogative “Howhigh is the temperature” as the requirement. Eventually, the obtainedresult is “requirement: How high is the temperature, place: Beijing,date: today”.

At S103, the service selecting device 30 analyzes thesemantically-analyzed query in terms of completeness by use of theservice mapping rule base 160, the user query history base 162 or thefact base 164 and then complement any lost content to obtain a servicewhich is selected from various services provided from the servicemapping rule base according to the user query. The retrieving device 40retrieves the corresponding answer based on the selected service atS104. The retrieving device may return only the answer corresponding tothe user query, as shown in FIG. 11 a, through a method comprising stepsof:

(1) information search, that is, finding the service providercorresponding to the service type in the selected service and thensending the service parameters in the selected service to the serviceprovider which will search and return a corresponding retrieval result;and

(2) answer generation, that is, generating the final answer according tothe retrieval result returned by the service provider. An integration isrequired for respective retrieval results if there are a number ofservice providers. The integration can be implemented in any relevantknown method, such as ranking these results based on the credit standingof each service provider.

Referring to the above example of the user query “How high is thetemperature in Beijing today?”, based on the selected service “servicetype: weather; place: Beijing; date: today”, the system can find serviceproviders corresponding to the service type “weather”, such as ChinaWeather Bureau, Weather Query Website and the like, send the serviceparameters “place: Beijing; date: today” to the service providers andreceive and integrate the retrieval results returned by them.

The retrieving device 40 can also return other relevant answers, asshown in FIG. 11 b in which there is a step of finding relevantservices, that is, finding other services relevant to the user query.For example, when a user queries how to get to a place, the system canprovide information of relevant services, such as weather, traffic andthe like in addition to providing a route. This can be realized in aprior art manner, for example, predefining a service relevancy table forrecording relevancy between different service types and then finding arelevant service type on the basis of the service relevancy table.

The retrieved answer is sent to the user terminal by the answer sender102 at S105.

In the present invention, the service selecting device 30 in the serviceselection system utilizes the service mapping rule base 160, the userquery history base 162 or the fact base 164 to determine the lostcontent in the query and therefore complement the lost content so as toselect the corresponding service. Accordingly, the following descriptionis made with respect to the structures of the service mapping rule base160, the user query history base 162 and the fact base 164 with respectto FIGS. 2 a, 2 b, 3 and 4. Then, a detailed explanation will be givento the inventive service selection system in connection to the servicemapping rule base, the user query history base and the fact base.

The service mapping rule base 160 stores multiple sets of servicemapping rules. When the match is established between the user's naturelanguage query and a service mapping rule in the service mapping rulebase 160, a service corresponding to the rule can be found. FIG. 2 ashows an example of the service mapping rule base 160. As shown in FIG.2 a, one piece of service mapping rule is generally composed of number,requirement, service type and service parameters. The requirementrepresents what is the question from the user query, that is, whatservice is related to the answer expected by the user. The service typedefines the service category to which the query question belongs. Theservice parameters describe service type and service invocationinterface, and the service provider can conduct retrieval based onservice parameters. Each piece of rule stored in the service mappingrule base 160 represents “when a user query conforms to a specifiedrequirement, which service type the query corresponds to and what is thecorresponding service parameter”.

Take the first piece of mapping rule in FIG. 2 a as an example, sincethe requirement in the user query is “how high is the temperature”, thequery corresponds to the service type “weather”, and the serviceparameter are place and date.

FIG. 2 b shows an example of a method for generating the service mappingrule base. First, a set of actual user queries is gathered fromrespective service providers. Then, a query corpus is established fromthe gathered user queries. Here, any semantic analyzing method in theprior art can be utilized to analyze each user query and obtain asemantic analysis result for the purpose of query corpus establishment.Lastly, the similarity between the marking results of all the queriesfor each service type is analyzed in the query corpus, and certainservice mapping rule is extract from the similarity and written into theservice mapping rule base.

For example, those frequently-asked queries, such as “how high is thetemperature in Beijing today?” or “how high is the temperature inShanghai tomorrow?”, are first gathered from weather service providers.Then a query corpus is established from semantic analysis resultsobtained through semantic analysis, and all queries related to theservice type “weather” are analyzed to extract the common requirement“how high is the temperature” as well as the common parameters “place”and “date” so as to finally generate a service mapping rule for“weather”. Although the above method generates the service mapping rulebase automatically, the base can be manually generated by summarizingvarious service mapping rules by an operator. Alternatively, the servicemapping rule base can be semi-automatically generated, that is, firstgenerating service mapping rules automatically, and then correcting themmanually.

FIG. 3 shows an example of the user query history base, which stores alluser query records. Generally, one piece of user query record consistsof user, query question, query time, service type and query parameter,and the query parameter can comprises a set of parameters each having acorresponding parameter value.

Take the first piece of user query record in FIG. 3 as an example, itrepresents that Tom made a query of “where is Beijing Hotel?” at 16:25on Aug. 2, 2007, in which the service type is “location”, the parameter“place” has a value of “Beijing Hotel”.

The user query history is generated automatically. To be specific, everytime processing on one user query is completed, the system stores, asone query record, the user, query question, query time and selectedservice.

FIG. 4 a shows an example of the fact base, which describes customary ordefault knowledge. As shown in FIG. 4 a, each fact is usually composedof number, service type, lost parameter and default value, andrepresents “when a user queries some service, what is the default valueof a parameter if it is lost”. Take the first fact as an example, whichmeans “when a user queries traffic service, the time is considered asnow (i.e., the current moment) by default if the user does not specifyany time.” The second fact represents “when a user queries weatherservice, the date is considered as today (i.e., this day) by default ifthe user does not specify any date.” The fact base is created primarilyby summarizing characteristics of respective services manually.

FIG. 5 a shows a schematic block diagram of the service selecting deviceaccording to the present invention. Generally speaking, a naturallanguage query inputted by a user may be incomplete and may lack somenecessary parameter values. For example, the user wants to make queriesof “how high is the temperature in Beijing today?” and “how can I get toBeijing Airport from Zhongguancun?”, while he actually inputs thequeries of “how high is the temperature in Beijing?” and “how can I getto Beijing Airport?”, which lack the parameter values “today” and “fromZhongguancun”, respectively. With the existing natural language basedservice selection system, such incomplete queries cannot be processedand thus the user cannot obtain any answer related to his or her desiredservice. On the other hand, the service selecting device in the presentinvention can obtain a selected service by complementing such incompletequeries automatically or semi-automatically. Therefore, a complete querycan be generated and the corresponding query answer can be provided tothe user with the application of the service selecting device in thepresent invention, even though the user inputs an incomplete query.

Now turning to FIG. 5 a, the service selecting device includes an inputsection (not shown) for receiving the semantically-analyzed user query,an automatic service selecting section 51 for complementingautomatically an incomplete query based on the user's natural languagequery utilizing at least one of the service mapping rule base 160, theuser query history base 162 or the fact base 164 so as to acquire aselected service, a semi-automatic service selecting section 52 forcomplementing an incomplete query through interaction with the user soas to acquire a selected service when the automatic service selectingsection 51 does not obtain the selected service, or for complementing anincomplete query in the case that the user considers the result from theautomatic service selecting section 51 not to comply with his or herquery, and an output section (not shown) for outputting the selectedservice.

FIG. 5 b shows a flowchart of the service selecting method according tothe present invention. At S501, the service selecting device receivesthe semantically-analyzed query. At S502, the automatic serviceselecting section 51 complements automatically the incomplete querybased on the user's natural language query by utilizing at least one ofthe service mapping rule base 160, the user query history base 162 orthe fact base 164 so as to acquire a selected service. When theautomatic service selecting section 51 does not obtain the selectedservice or the user considers the result from the automatic serviceselecting section 51 does not comply with his or her query, thesemi-automatic service selecting section 52 at S503 complements theincomplete query through interaction with the user so as to acquire theselected service. FIG. 6 a shows a detailed block diagram of thesemi-automatic service selecting section 52 according to the presentinvention. The semi-automatic service selecting section 52 comprises aninput unit 61 for receiving a semantic analysis result obtained fromanalysis of a natural language query, a lost content searching unit 62for matching the semantic analysis result with the service mapping rulein the service mapping rule base, finding a matched service mapping ruleand extracting from it the service type and the service parameter lostin the query, a user interacting unit 63 for interacting with the userand acquiring the feedback information from the user, a parameter valueextracting unit 64 for extracting the lost parameter valued from thefeedback information of the user, a query complementing unit 65 foradding the service type, the lost service parameter and the lostparameter value into the semantic analysis result to complement theincomplete query so as to generate the selected service, and an outputunit 66 for outputting the selected service after the abovecomplementation.

FIG. 6 b shows a flowchart of the semi-automatic service selectingmethod. At S601, the input unit 61 receives a semantically-analyzedquery in natural language from the user terminal. The lost contentsearching unit 62 at S602 matches the semantic analysis result with theservice mapping rule in the service mapping rule base, finds a matchedservice mapping rule and determines the lost service parameter. Then, itextracts from the matched service mapping rule the service type to whichthe query belongs and the service parameter lost in the query. Here, thematching scheme used by the lost content searching unit includes: (1)the requirement of the service mapping rule is identical to therequirement of the semantic analysis result; and (2) the serviceparameter of the service mapping rule contains the parameter of semanticanalysis result. At S603, prompt information is generated to prompt theuser to input the lost parameter value based on the lost parameter foundin S602 and sent to the user, and the feedback information is in turnreceived from the user with respect to the prompt information. Since thefeedback information of the user may contain some words other than thelost parameter value, the latter needs to be extracted from the feedbackinformation at S604 after the reception of the user feedback. Here, thesame semantic marking method as used in the above semantic analysis canbe utilized to semantically mark the feedback information of the userand find words corresponding to the lost parameter as the lost parametervalue. At S605, the query complementing unit 65 fills the service type,the lost service parameter and the lost parameter value into thesemantic analysis result to complement the incomplete query and generatethe selected service. Lastly, the output unit 66 outputs the selectedservice at S606. Automatic service selection can be performed inaddition to the above semi-automatic service selection throughinteraction with the user.

FIG. 6 c shows an example of automatic service selection, in which theuser query is “how high is the temperature in Beijing?”, and thesemantic analysis result is “requirement: how high is the temperature;place: Beijing”.

-   -   First step of searching lost content: the first rule in the        service mapping rule base has the same requirement “how high is        the temperature” as that of the semantic analysis result, and        the service parameters “<place>;<date>” contain the parameter        “place” of the semantic analysis result, the first rule is        therefore taken as a matched rule, with the service type        “weather” being extracted and “date” being the parameter lost in        the query;    -   Second step of interacting with the user: generating prompt        information “which date do you want to specify in your weather        query?” and sending it to the user, and then receiving the user        feedback “I want to query today's weather”;    -   Third step of extracting the parameter value: taking “today” in        the user feedback as the lost parameter value since this word        belongs to the lost parameter “date”;    -   Final step of complementing the query: adding the service type        “weather”, the lost parameter “date” and the lost parameter        value “today” into the semantic analysis result to obtain a        selected service “service type: weather; place: Beijing; date:        today”.

FIG. 7 a shows the first embodiment of the automatic service selectingsection 61 according to the present invention, which complements anincomplete natural language query from a user based on a current userquery history base. The automatic service selecting section 61 includesan input unit 71 for receiving the inputted semantic analysis result, acurrent user query history base 77 storing all records of queries by auser who is exactly the user of the current query, a lost contentsearching unit 72 for matching the semantic analysis result with theservice mapping rule in the service mapping rule base, finding a matchedservice mapping rule, determines the lost service parameter, andextracting from the matched service mapping rule the service type towhich the query belongs and the service parameter lost in the query, alatest query detecting unit 73 for detecting the history of the latestuser query, i.e., the last query made by the user, and extracting thecorresponding lost parameter value if the parameter lost in the currentquery is contained in the latest user query, a similar query detectingunit 74 for searching the history query that contains the lost parameterin the current query as a similar query from the current user queryhistory base 77 when there is no parameter value extracted by the latestquery detecting unit 73, and extracting the parameter valuecorresponding to the lost parameter in current query from the similarquery if it contains the parameter lost in the current query, a querycomplementing unit 75 for adding the service type, the lost parameterand the parameter value into the semantic analysis query so as to obtainthe selected service, and an output unit 76 for outputting the selectedservice.

FIG. 7 b shows a flowchart of the first embodiment of the automaticservice selecting method. At S701, the input unit 71 receives asemantically-analyzed query in natural language from the user. The lostcontent searching unit 72 at S702 matches the semantic analysis resultwith the service mapping rule in the service mapping rule base 160,finds a matched service mapping rule and determines the lost serviceparameter. Then, it extracts from the matched service mapping rule theservice type to which the query belongs and the service parameter lostin the query. Here, the matching scheme used by the lost contentsearching unit 72 is the same as that used in the method shown in FIG. 6b.

At S703, the latest query detecting unit 73 searches the last query madeby the user. Detecting the latest query, i.e., the last query made bythe user, at first can accelerate the query process, since the user mayomit some words while making several queries successively. The detailedmethod is: finding the latest query made by the user in the current userquery history base 77, with a query interval being set to be smallerthan a particular threshold; checking whether the latest query containsthe parameter lost in the current query; if the answer is Yes,extracting the corresponding parameter value as the lost parameter valueand then performing the step S705; otherwise, performing the step S704.

At S704, the similar query detecting unit 74 searches the current userquery history base for a query similar to the current query, andextracts the parameter value corresponding to the lost parameter incurrent query from the similar query if it contains the parameter lostin the current query. The determination of the similar query is madesuch that a query is considered as the similar query if (1) the servicetype obtained at the lost content searching step is identical to that ofthe history query in the current user query history base; and/or (2) thequery parameter of the history query contains the query parameter of thesemantically-analyzed query (preferably, both the queries have the sameparameter value).

At S705, the service type, the lost parameter and the parameter valueare added into the semantic analysis query to obtain the selectedservice. Finally, the selected service is outputted at S706.

FIG. 7 c shows an example of the implementation of service selection, inwhich the user Tom makes a query of “how to contact?”, and the semanticanalysis result is “requirement: how to contact”.

-   -   First step of searching lost content: the second rule in the        service mapping rule base has the same requirement “how to        contact” as that of the semantic analysis result, and there is        no parameter in the semantic analysis result, the second rule is        therefore taken as a matched rule, with the service type        “telephone” being extracted and the service parameter “place”        being the parameter lost in the query;    -   Second step of detecting the latest query: since the last query        made by Tom is “where is Beijing Hotel?”, and the query        parameter is “place: Beijing Hotel” which contains the lost        parameter “place”, the corresponding parameter value “Beijing        Hotel” is extracted;    -   No similar query is detected when the above latest query        detection is successful;    -   Final step of complementing the query: adding the service type        “telephone”, the lost parameter “place” and the lost parameter        value “Beijing Hotel” into the semantic analysis result to        obtain a selected service “service type: telephone; place:        Beijing Hotel”.

FIG. 8 a shows the second embodiment of the automatic service selectionsection according to the present invention.

This automatic service selection section of the second embodimentcomplements a natural language query from a user based on other userquery history base. Such service selection section comprises an inputunit 81 for receiving the inputted semantic analysis result, an otheruser query history base 86 storing all records of queries by otherusers, a lost content searching unit 82 for matching the semanticanalysis result with the service mapping rule in the service mappingrule base, finding a matched service mapping rule, determines the lostservice parameter, and extracting from the matched service mapping rulethe service type to which the query belongs and the service parameterlost in the query, a similar query detecting unit 83 for searching aquery similar to the current query from the other user query historybase 86 and extracting the parameter value from the similar query as theparameter value lost in the current query, a query complementing unit 84for adding the service type, the lost parameter and the parameter valueinto the semantic analysis query so as to obtain the selected service,and an output unit 85 for outputting the selected service.

FIG. 8 b shows a flowchart of the second embodiment of the automaticservice selecting method.

At S801, the input unit 81 receives a semantically-analyzed query innatural language from the user. The lost content searching unit 82 atS802 matches the semantic analysis result with the service mapping rulein the service mapping rule base 160, finds a matched service mappingrule and determines the lost service parameter. Then, it extracts fromthe matched service mapping rule the service type to which the querybelongs and the service parameter lost in the query. Here, the matchingscheme used by the lost content searching unit 82 is the same as thatused in the method shown in FIG. 6 b.

At S803, the similar query detecting unit 83 searches the other userquery history base for a query similar to the current query, andextracts the parameter value corresponding to the lost parameter incurrent query from the similar query if it contains the parameter lostin the current query. The determination of the similar query is madesuch that a query is considered as the similar query if (1) the servicetype obtained at the lost content searching step is identical to that ofthe history query in the other user query history base; and/or (2) thequery parameter of the history query contains the query parameter of thesemantically-analyzed query (preferably, both the queries have the sameparameter value).

At S804, the service type, the lost parameter and the parameter valueare added into the semantic analysis query to obtain the selectedservice. Finally, the selected service is outputted at S805.

FIG. 8 c shows an example of the implementation of service selection, inwhich the user makes a query of “how high is the temperature inBeijing?”, and the semantic analysis result is “requirement: how high isthe temperature; place: Beijing”.

-   -   First step of searching lost content: the first rule in the        service mapping rule base has the same requirement “how high is        the temperature” as that of the semantic analysis result, and        the service parameters “<place>;<date>” contain the parameter        “place” of the semantic analysis result, the first rule is        therefore taken as a matched rule, with the service type        “weather” being extracted and “date” being the parameter lost in        the query;    -   Second step of detecting the similar query: there exists a query        made by another user John, “how high is the temperature in        Beijing today?”, in which the service type is “weather”, the        query parameter “place: Beijing; date: today” contains the        parameter “place” in the semantic analysis result, and both of        the parameter values are “Beijing”, this query is thus regarded        as the similar query; since the similar query contains the lost        parameter “date”, the corresponding parameter value “today” is        extracted;    -   Final step of complementing the query: adding the service type        “weather”, the lost parameter “date” and the lost parameter        value “today” into the semantic analysis result to obtain a        selected service “service type: weather; place: Beijing; date:        today”.

FIG. 9 a is a schematic diagram showing the third embodiment of theautomatic service selection section according to the present invention.

This automatic service selection section of the third embodimentcomplements a natural language query from a user based on a fact base.

Such service selection section comprises an input unit 91 for receivingthe inputted semantic analysis result, a lost content searching unit 92for matching the semantic analysis result with the service mapping rulein the service mapping rule base, finding a matched service mappingrule, determines the lost service parameter based on the matched servicemapping rule, and extracting from the matched service mapping rule theservice type to which the query belongs and the service parameter lostin the query, a fact matching unit 93 for matching the semantic analysisresult with each fact in the fact base 96, finding a matched fact andextracting the default value in the matched fact as the parameter valuelost in the current query, a query complementing unit 94 for adding theservice type, the lost parameter and the parameter value of the lostparameter into the semantic analysis query so as to obtain the selectedservice, and an output unit 95 for outputting the selected service.

FIG. 9 b shows a flowchart of the third embodiment of the automaticservice selecting method according to this invention.

At S901, the input unit 91 receives a semantically-analyzed query innatural language from the user. The lost content searching unit 92 atS902 matches the semantic analysis result with the service mapping rulein the service mapping rule base 160, finds a matched service mappingrule and determines the lost service parameter. Then, it extracts fromthe matched service mapping rule the service type to which the querybelongs and the service parameter lost in the query. Here, the matchingscheme used by the lost content searching unit 92 is the same as thatused in the method shown in FIG. 6 b.

At S903, the fact matching unit 93 finds a matched fact by matching thesemantic analysis result with each fact in the fact base 96 andextracting the default value in the matched fact as the parameter valuelost in the current query. The determination of the matched fact is madesuch that a fact is considered as the matched query if (1) the servicetype obtained at the lost content searching step is identical to that ofa fact in the fact base; and/or (2) the lost parameter in the fact isidentical to that obtained at the lost content searching step.

At S904, the service type, the lost parameter and the parameter value ofthe lost parameter are added into the semantic analysis result to obtainthe selected service. Finally, the selected service is outputted atS905.

FIG. 9 c shows an example of the implementation of service selection, inwhich the user makes a query of “how high is the temperature inBeijing?”, and the semantic analysis result is “requirement: how high isthe temperature; place: Beijing”.

-   -   First step of searching lost content: the first rule in the        service mapping rule base has the same requirement “how high is        the temperature” as that of the semantic analysis result, and        the service parameters “<place>;<date>” contain the parameter        “place” of the semantic analysis result, the first rule is        therefore taken as a matched rule, with the service type        “weather” being extracted and “date” being the parameter lost in        the query;    -   Second step of fact matching: the service type of the second        fact is “weather”, and the lost parameter is also “date”, this        fact is thus regarded as the matched fact, and the default value        “today” is extracted from the fact;    -   Final step of complementing the query: adding the service type        “weather”, the lost parameter “date” and the lost parameter        value “today” into the semantic analysis result to obtain a        selected service “service type: weather; place: Beijing; date:        today”.

FIG. 10 a shows a natural language based service query system accordingto an embodiment of the present invention. The difference between FIGS.10 a and 1 a is that the natural language based service query system inFIG. 10 a further comprises a determining device 70 and a complete queryprocessing device 80. The determining device 70 determines whether aquery inputted by a user is complete. The query will be processed by theservice selecting device 30 if it is determined as incomplete, otherwisethe query will be transferred to the complete query processing device80.

The determining device 70 determines whether a rule exactly matched withthe semantic analysis result of a user query can be found by comparingthe semantic analysis result with all service mapping rules, and, if amatched rule is found, then sends the semantic analysis result and thenumber of the matched rule to the complete query processing device 80;otherwise sends the semantic analysis result to the service selectingdevice 30. Here, a rule can be regarded as a matched rule if it meetsthe following conditions:

(1) the rule has the same requirement as that of the semantic analysisresult;

(2) all service parameters required by the rule are contained in thesemantic analysis result.

As shown in FIG. 10 b, the user makes a query of “how high is thetemperature in Beijing today?”, and the semantic analysis result is“requirement: how high is the temperature; place: Beijing; date; today”.This result is exactly matched with the fist rule in the service mappingrule base, since the requirement of the result is the same as that ofthe rule, and the result contains all the parameters “place” and “date”of the rule. Therefore, the semantic analysis result and the number ofthe matched rule are sent to the complete query processing device 80together.

The complete query processing device 80 is adapted to process thosecomplete (without any lost content) queries to acquire the selectedservice. It finds out a matched rule from the service mapping rule baseaccording to the number of the matched rule obtained by the determiningunit, extract the service type and then generate the selected service bycombining the semantic analysis result. Here, a selected service usuallycomprises one service type and a set of service parameters.

Take as an example the user query of “how high is the temperature inBeijing today?”, it is exactly matched with the first rule in theservice mapping rule base. Thus, the service type “weather” of this ruleis extracted and the selected service is generated as “service type:weather; place: Beijing; date: today”.

In conclusion, the above service query system can process both anincomplete query and a complete query, and thus find out an answercorresponding to the incomplete or complete query.

FIGS. 12 a and 12 b show a schematic diagram for applying the serviceselecting device according to the present invention to a mobile terminaland an ASP (Active Server Page), respectively. As shown in FIG. 12 a,the semantic analyzing device, the service selecting device and theretrieving device can be embedded together into the mobile terminal. Nowturning to FIG. 12 b, the semantic analyzing device, the serviceselecting device and the retrieving device can also be embedded into theASP so that the user can be provided with more convenient and rapidquery service.

While the present invention has been described with reference to theabove particular embodiments, the present invention should be defined bythe appended claims other than these specific embodiments. It is obviousto those ordinarily skilled in the art that any change or modificationcan be made without departing from the scope and spirit of the presentinvention.

What is claimed is:
 1. A natural language based service selection systemfor complementing incomplete queries, comprising: a semantic analyzingdevice which analyzes an incomplete query from a user semantically; aservice selecting device which complements the incomplete query based ona semantically-analyzed query to complete the incomplete query, andselects a corresponding selected service from among a plurality ofservices based on a complete query; a retrieving device which retrievesan answer according to the corresponding selected service; and at leastone processor coupled to the semantic analyzing device, the serviceselecting device and the retrieving device, wherein the serviceselecting device comprises a semi-automatic service selecting sectionwhich searches lost content in the incomplete query by using a servicemapping rule base and complements the lost content through aninteracting with the user, wherein the semi-automatic service selectingsection comprises: a lost content searching unit which matches thesemantically-analyzed query with a service mapping rule in the servicemapping rule base, and extracts a service type to which the incompletequery belongs and a lost parameter in the incomplete query; a userinteracting unit which prompts the user to input prompt information of aparameter value corresponding to the lost parameter and receivesfeedback information including the parameter value from the user; aparameter value extracting unit which extracts the parameter value fromthe feedback information of the user; and a query complementing unitwhich adds the service type, the lost parameter and the parameter valueinto the semantically-analyzed query to form the complete query.
 2. Thenatural language based service selection system according to claim 1,wherein the lost content searching unit retrieves the service mappingrule meeting the following conditions as matched service mapping rulefrom the service mapping rule base: a requirement of the service mappingrule is identical to a requirement of the semantically-analyzed query;and a service parameter of the service mapping rule contains a parameterof the semantically-analyzed query.
 3. The natural language basedservice selection system according to claim 1, wherein the parametervalue extracting unit retrieves the parameter value corresponding to thelost parameter by semantically marking the feedback information of theuser.
 4. The natural language based service selection system accordingto claim 1, wherein the service selecting device comprises: an automaticservice selecting section which searches lost content in a current queryby using the service mapping rule base and complements the lost contentthrough searching a current user query history base or an other userquery history base or a fact base to form the complete query; and thesemi-automatic service selecting section which searches the lost contentin the current query by using the service mapping rule base andcomplements the lost content through interacting with the user to formthe complete query, when the corresponding selected service generated bythe automatic service selecting section is not accurate.
 5. A naturallanguage based service selection system for complementing incompletequeries, comprising: a semantic analyzing device which analyzes anincomplete query from a user semantically; a service selecting devicewhich complements the incomplete query based on a semantically-analyzedquery to complete the incomplete query, and selects a correspondingselected service from among a plurality of services based on a completequery; a retrieving device which retrieves an answer according to thecorresponding selected service; and at least one processor coupled tothe semantic analyzing device, the service selecting device and theretrieving device, wherein the service selecting device comprises anautomatic service selecting section which searches lost content in acurrent query by using a service mapping rule base and complements thelost content through searching a current user query history base,wherein the automatic service selecting section comprises: a lostcontent searching unit which matches the semantically-analyzed querywith a service mapping rule in the service mapping rule base, andextracts a service type to which the current query belongs and a lostparameter in the current query; a latest query detecting unit whichsearches a latest query contained the lost parameter in the currentquery from the current user query history base, and extracts theparameter value corresponding to the lost parameter in the currentquery; and a query complementing unit which adds the service type, thelost parameter and the parameter value into the semantically-analyzedquery to form the complete query.
 6. The natural language based serviceselection system according to claim 5, wherein the automatic serviceselecting section further comprises a similar query detecting unit whichsearches a history query that contains the lost parameter in the currentquery as a similar query from the current user query history base whenthere is no parameter value extracted by the latest query detectingunit, and extracts the parameter value corresponding to the lostparameter in the current query from the similar query.
 7. The naturallanguage based service selection system according to claim 6, whereinthe similar query detecting unit retrieves the history query meeting thefollowing conditions as a similar query from the current user queryhistory base: a service type of the history query is identical to aservice type to which the current query belongs; and a query parameterof the history query contains a query parameter of thesemantically-analyzed query.
 8. The natural language based serviceselection system according to claim 5, wherein the lost contentsearching unit retrieves the service mapping rule meeting the followingconditions as a matched service mapping rule from the service mappingrule base: a requirement in the service mapping rule is identical to arequirement of the semantically-analyzed query; and a service parameterin the service mapping rule contains a parameter of thesemantically-analyzed query.
 9. A natural language based serviceselection system for complementing incomplete queries, comprising: asemantic analyzing device which analyzes an incomplete query from a usersemantically; a service selecting device which complements theincomplete query based on a semantically-analyzed query to complete theincomplete query, and selects a corresponding selected service fromamong a plurality of services based on a complete query; a retrievingdevice which retrieves an answer according to the corresponding selectedservice; and at least one processor coupled to the semantic analyzingdevice, the service selecting device and the retrieving device, whereinthe service selecting device comprises an automatic service selectingsection which searches lost content in a current query by using aservice mapping rule base and complements the lost content throughsearching an other user query history base, wherein the automaticservice selecting section comprises: a lost content searching unit whichmatches the semantically-analyzed query with a service mapping rule inthe service mapping rule base, and extracts a service type to which thecurrent query belongs and a lost parameter in the current query; asimilar query detecting unit which searches a history query thatcontains the lost parameter in the current query as a similar query fromthe other user query history base, and extracts a parameter valuecorresponding to the lost parameter in the current query from thesimilar query; and a query complementing unit which adds the servicetype, the lost parameter and the parameter value into thesemantically-analyzed query to form the complete query.
 10. The naturallanguage based service selection system according to claim 9, whereinthe lost content searching unit retrieves the service mapping rulemeeting the following conditions as matched service mapping rule fromthe service mapping rule base: a requirement in the service mapping ruleis identical to a requirement of the semantically-analyzed query; and aservice parameter in the service mapping rule contains a parameter ofthe semantically-analyzed query.
 11. The natural language based serviceselection system according to claim 9, wherein the similar querydetecting unit retrieves the history query meeting the followingconditions as a similar query from the other user query history base: aservice type of the history query is identical to the service type towhich the current query belongs; and a query parameter of the historyquery contains a query parameter of the semantically-analyzed query. 12.A natural language based service selection system for complementingincomplete queries, comprising: a semantic analyzing device whichanalyzes an incomplete query from a user semantically; a serviceselecting device which complements the incomplete query based on asemantically-analyzed query to complete the incomplete query, andselects a corresponding selected service from among a plurality ofservices based on a complete query; a retrieving device which retrievesan answer according to the corresponding selected service; and at leastone processor coupled to the semantic analyzing device, the serviceselecting device and the retrieving device, wherein the serviceselecting device comprises a third an automatic service selectingsection which searches lost content in a current query by using aservice mapping rule base and complements the lost content throughsearching a fact base, wherein the automatic service selecting sectioncomprises: a lost content searching unit which matches thesemantically-analyzed query with a service mapping rule in the servicemapping rule base, and extracts a service type to which the incompletequery belongs and a lost parameter in the incomplete query; a factmatching unit which matches the semantically-analyzed query with a factin a fact base to find a matched fact, and extracts a fault value fromthe matched fact as a lost parameter value; and a query complementingunit which adds the service type, the lost parameter and the lostparameter value into the semantically-analyzed query to form thecomplete query.
 13. The natural language based service selection systemaccording to claim 12, wherein the lost content searching unit retrievesthe service mapping rule meeting the following conditions as a matchedservice mapping rule from the service mapping rule base: a requirementin the service mapping rule is identical to a requirement of thesemantically-analyzed query; and a service parameter in the servicemapping rule contains a parameter of the semantically-analyzed query.14. The natural language based service selection system according toclaim 12, wherein the fact matching unit retrieves the fact meeting thefollowing conditions as matched fact from the fact base: a service typeof the fact is identical to the service type to which the query belongs;and a lost parameter in the fact is identical to the lost parameter inthe query.
 15. A method of selecting service based on an incompletequery, comprising: a semantic analyzing step, performed by at least oneprocessor, of analyzing an incomplete query from a user semantically; aservice selecting step, performed by the at least one processor, ofcomplementing the incomplete query based on a semantically-analyzedquery to complete the incomplete query, and selecting a correspondingselected service from among a plurality of services based on a completequery; and a retrieving step, performed by the at least one processor,of retrieving an answer according to the corresponding selected service,wherein the service selecting step comprises a semi-automatic serviceselecting step of searching lost content in the incomplete query byusing a service mapping rule base and complementing the lost contentthrough an interacting with the user, wherein the semi-automatic serviceselecting step comprises: a lost content searching step of matching thesemantically-analyzed query with a service mapping rule in the servicemapping rule base, and extracting a service type to which the incompletequery belongs and a lost parameter in the incomplete query; a userinteracting step of prompting the user to input prompt information of aparameter value corresponding to the lost parameter and receivingfeedback information including the parameter value from the user; aparameter value extracting step of extracting the parameter value fromthe feedback information of the user; and a query complementing step ofadding the service type, the lost parameter and the parameter value intothe semantically-analyzed query to form the complete query.
 16. Themethod according to claim 15, wherein the lost content searching stepcomprises a step of retrieving the service mapping rule meeting thefollowing conditions as matched service mapping rule from the servicemapping rule base: a requirement of the service mapping rule isidentical to a requirement of the semantically-analyzed query; and aservice parameter of the service mapping rule contains a parameter ofthe semantically-analyzed query.
 17. The method according to claim 15,wherein the parameter value extracting step retrieves the parametervalue corresponding to the lost parameter by semantically marking thefeedback information of the user.
 18. The method according to claim 15,wherein the service selecting step comprises: an automatic serviceselecting step of searching lost content in a current query by using theservice mapping rule base and complements the lost content throughsearching a current user query history base or an other user queryhistory base or a fact base to form the complete query; and thesemi-automatic service selecting step of searching the lost content inthe current query by using the service mapping rule base andcomplementing the lost content through interacting with the user to formthe complete query, when the corresponding selected service generated bythe automatic service selecting step is not accurate.
 19. A method ofselecting service based on an incomplete query, comprising: a semanticanalyzing step, performed by at least one processor, of analyzing anincomplete query from a user semantically; a service selecting step,performed by the at least one processor, of complementing the incompletequery based on a semantically-analyzed query to complete the incompletequery, and selecting a corresponding selected service from among aplurality of services based on a complete query; and a retrieving step,performed by the at least one processor, of retrieving an answeraccording to the corresponding selected service, wherein the serviceselecting step comprises an automatic service selecting step ofsearching lost content in a current query by using a service mappingrule base and complementing the lost content through searching a currentuser query history base, wherein the automatic service selecting stepcomprises: a lost content searching step of matching thesemantically-analyzed query with a service mapping rule in the servicemapping rule base, and extracting a service type to which the currentquery belongs and a lost parameter in the current query; a latest querydetecting step of searching a latest query contained the lost parameterin the current query from the current user query history base, andextracting the parameter value corresponding to the lost parameter inthe current query; and a query complementing step of adding the servicetype, the lost parameter and the parameter value into thesemantically-analyzed query to form the complete query.
 20. The methodaccording to claim 19, wherein the automatic service selecting stepfurther comprises a similar query detecting step of searching a historyquery that contains the lost parameter in the current query as a similarquery from the current user query history base when there is noparameter value extracted by the latest query detecting step, andextracting the parameter value corresponding to the lost parameter inthe current query from the similar query.
 21. The method according toclaim 20, wherein the similar query detecting step comprises a step ofretrieving the history query meeting the following conditions as asimilar query from the current user query history base: a service typeof the history query is identical to a service type to which the currentquery belongs; and a query parameter of the history query contains aquery parameter of the semantically-analyzed query.
 22. The methodaccording to claim 19, wherein the lost content searching step comprisesa step of retrieving the service mapping rule meeting the followingconditions as a matched service mapping rule from the service mappingrule base: a requirement in the service mapping rule is identical to arequirement of the semantically-analyzed query; and a service parameterin the service mapping rule contains a parameter semantically-analyzedquery.
 23. A method of selecting service based on an incomplete query,comprising: a semantic analyzing step, performed by at least oneprocessor, of analyzing an incomplete query from a user semantically; aservice selecting step, performed by the at least one processor, ofcomplementing the incomplete query based on a semantically-analyzedquery to complete the incomplete query, and selecting a correspondingselected service from among a plurality of services based on a completequery; and a retrieving step, performed by the at least one processor,of retrieving an answer according to the corresponding selected service,wherein the service selecting step comprises an automatic serviceselecting step of searching lost content in a current query by using aservice mapping rule base and complementing the lost content throughsearching an other user query history base, wherein the automaticservice selecting step comprises: a lost content searching step ofmatching the semantically-analyzed query with a service mapping rule inthe service mapping rule base, and extracting a service type to whichcurrent query belongs and a lost parameter in the current query; asimilar query detecting step of searching a history query that containsthe lost parameter in the current query as a similar query from theother user query history base, and extracting a parameter valuecorresponding to the lost parameter in the current query from thesimilar query; and a query complementing step of adding the servicetype, the lost parameter and the parameter value into thesemantically-analyzed query to form the complete query.
 24. The methodaccording to claim 23, wherein the lost content searching step comprisesa step of retrieving the service mapping rule meeting the followingconditions as matched service mapping rule from the service mapping rulebase: a requirement in the service mapping rule is identical to arequirement of the semantically-analyzed query; and a service parameterin the service mapping rule contains a parameter of thesemantically-analyzed query.
 25. The method according to claim 23,wherein the similar query detecting step comprises a step of retrievingthe history query meeting the following conditions as a similar queryfrom the other user query history base: a service type of the historyquery is identical to the service type to which the current querybelongs; and a query parameter of the history query contains a queryparameter of the semantically-analyzed query.
 26. A method of selectingservice based on an incomplete query, comprising: a semantic analyzingstep, performed by at least one processor, of analyzing an incompletequery from a user semantically; a service selecting step, performed bythe at least one processor, of complementing the incomplete query basedon a semantically-analyzed query to complete the incomplete query, andselecting a corresponding selected service from among a plurality ofservices based on a complete query; and a retrieving step, performed bythe at least one processor, of retrieving an answer according to thecorresponding selected service, wherein the service selecting stepcomprises an automatic service selecting step of searching lost contentin a current query by using a service mapping rule base andcomplementing the lost content through searching a fact base, whereinthe automatic service selecting step comprises: a lost content searchingstep of matching the semantically-analyzed query with a service mappingrule in the service mapping rule base, and extracting a service type towhich the incomplete query belongs and a lost parameter in theincomplete query; a fact matching step of matching thesemantically-analyzed query with a fact in a fact base to find a matchedfact, and extracting a fault value from the matched fact as a lostparameter value; and a query complementing step of adding the servicetype, the lost parameter and the lost parameter value into thesemantically-analyzed query to form the complete query.
 27. The methodaccording to claim 26, wherein the lost content searching step ofretrieving the service mapping rule meeting the following conditions asa matched service mapping rule from the service mapping rule base: arequirement in the service mapping rule is identical to a requirement ofthe semantically-analyzed query; and a service parameter in the servicemapping rule contains a parameter of the semantically-analyzed query.28. The method according to claim 26, wherein the fact matching step ofretrieving the fact meeting the following conditions as matched factfrom the fact base: a service type of the fact is identical to theservice type to which the current query belongs; and a lost parameter inthe fact is identical to the lost parameter in the current query.
 29. Aquery system, comprising: a query receiver which receives a user query;a semantic analyzing device which parses the user query and semanticallyanalyzes the user query; a service selecting device which complements anincomplete query based on the semantically-analyzed query to form thecomplete query, and selects a corresponding selected service from amonga plurality of services based on a complete query; a retrieving devicewhich retrieves an answer according to the corresponding selectedservice; an answer sender which sends the answer to a user; and at leastone processor coupled to the query receiver, the semantic analyzingdevice, the service selecting device, the retrieving device and theanswer sender, wherein the service selecting device comprises asemi-automatic service selecting section which searches lost content inthe incomplete query by using a service mapping rule base andcomplements the lost content through an interacting with the user,wherein the semi-automatic service selecting section comprises: a lostcontent searching unit which matches the semantically-analyzed querywith a service mapping rule in the service mapping rule base, andextracts a service type to which the incomplete query belongs and a lostparameter in the incomplete query; a user interacting unit which promptsthe user to input prompt information of a parameter value correspondingto the lost parameter and receives feedback information including theparameter value from the user; a parameter value extracting unit whichextracts the parameter value from the feedback information of the user;and a query complementing unit which adds the service type, the lostparameter and the parameter value into the semantically-analyzed queryto form the complete query.
 30. A query method, comprising: a queryreceiving step, performed by at least one processor, of receiving a userquery; a semantic analyzing step of parsing the user query andsemantically analyzes the user query; a service selecting step,performed by the at least one processor, of complementing an incompletequery based on the semantically-analyzed query to complete theincomplete query, and selecting a corresponding selected service fromamong a plurality of services based on a complete query; a retrievingstep, performed by the at least one processor, of retrieving an answeraccording to the corresponding selected service; and an answer sendingstep, performed by the at least one processor, of sending the answer toa user, wherein the service selecting step comprises a semi-automaticservice selecting step of searching lost content in the incomplete queryby using a service mapping rule base and complementing the lost contentthrough an interacting with the user, wherein the semi-automatic serviceselecting step comprises: a lost content searching step of matching thesemantically-analyzed query with a service mapping rule in the servicemapping rule base, and extracting a service type to which the incompletequery belongs and a lost parameter in the incomplete query; a userinteracting step of prompting the user to input prompt information of aparameter value corresponding to the lost parameter and receivingfeedback information including the parameter value from the user; aparameter value extracting step of extracting the parameter value fromthe feedback information of the user; and a query complementing step ofadding the service type, the lost parameter and the parameter value intothe semantically-analyzed query to form the complete query.
 31. A querysystem, comprising: a query receiver which receives a user query; asemantic analyzing device which parses the user query and semanticallyanalyzes the user query; a determining device which determines whetherthe user query is a complete user query; a first service selectingdevice which performs a process on the complete user query and selects afirst selected service from among a plurality of services based on thecomplete user query; a second service selecting device which complementsan incomplete user query to complete the incomplete user query andselects a second selected service from among the plurality of servicesbased on the completed incomplete user query; a retrieving device whichretrieves an answer according to the first selected service or thesecond selected service; an answer sender which sends the answer to auser; and at least one processor coupled to the query receiver, thesemantic analyzing device, the determining device, the first serviceselecting device, the second service selecting device, the retrievingdevice and the answer sender, wherein the second service selectingdevice comprises a semi-automatic service selecting section whichsearches lost content in the incomplete query by using a service mappingrule base and complements the lost content through an interacting withthe user, wherein the semi-automatic service selecting sectioncomprises: a lost content searching unit which matches thesemantically-analyzed query with a service mapping rule in the servicemapping rule base, and extracts a service type to which the incompletequery belongs and a lost parameter in the incomplete query; a userinteracting unit which prompts the user to input prompt information of aparameter value corresponding to the lost parameter and receivesfeedback information including the parameter value from the user; aparameter value extracting unit which extracts the parameter value fromthe feedback information of the user; and a query complementing unitwhich adds the service type, the lost parameter and the parameter valueinto the semantically-analyzed query to form the complete query.
 32. Thequery system according to claim 31, wherein the determining devicematches the semantically-analyzed user query with the service mappingrule in the service mapping rule base, if a matching is successful, theuser query is sent to the first service selecting device; and if thematching is not successful, the user query is sent to the second serviceselecting device.
 33. The query system according to claim 32, whereinthe determining device determines whether the matching is successfulaccording to the following conditions: a requirement of the servicemapping rule is identical to a requirement of the semantically-analyzedquery; and a service parameter of the service mapping rule contains aparameter of the semantically-analyzed query.
 34. A query method,comprising: a query receiving step, performed by at least one processor,of receiving a user query; a semantic analyzing step, performed by theat least one processor, of parsing the user query and semanticallyanalyzes the user query; a determining step, performed by the at leastone processor, of determining whether the user query is a complete userquery; a first service selecting step, performed by the at least oneprocessor, of performing a process on the complete user query andselecting a first selected service from among a plurality of servicesbased on the complete user query; a second service selecting step,performed by the at least one processor, of complementing an incompletequery to complete the incomplete user query and selecting a secondselected service from among the plurality of services based on thecompleted incomplete user query; a retrieving step, performed by the atleast one processor, of retrieving an answer according to the firstselected service or the second selected service; and an answer sendingstep, performed by the at least one processor, of sending the answer toa user, wherein the second service selecting step comprises asemi-automatic service selecting step of searching lost content in theincomplete query by using a service mapping rule base and complementingthe lost content through an interacting with the user, wherein thesemi-automatic service selecting step comprises: a lost contentsearching step of matching the semantically-analyzed query with aservice mapping rule in the service mapping rule base, and extracting aservice type to which the incomplete query belongs and a lost parameterin the incomplete query; a user interacting step of prompting the userto input prompt information of a parameter value corresponding to thelost parameter and receiving feedback information including theparameter value from the user; a parameter value extracting step ofextracting the parameter value from the feedback information of theuser; and a query complementing step of adding the service type, thelost parameter and the parameter value into the semantically-analyzedquery to form the complete query.
 35. The query method according toclaim 34, wherein the determining step comprises a step of matching thesemantically-analyzed user query with the service mapping rule in theservice mapping rule base, if the matching is successful, the user queryis processed by the first service selecting step; and if the matching isnot successful, the user query is processed by the second serviceselecting step.
 36. The query method according to claim 35, wherein thedetermining step determines whether the matching is successful accordingto the following conditions: a requirement of the service mapping ruleis identical to a requirement of the semantically-analyzed query; and aservice parameter of the service mapping rule contains a parameter ofthe semantically-analyzed query.